Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system’s memory, resulting in savings in the PV investment.

In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimated using lognormal and Weibull probability distribution functions respectively, after simulation of 8000 Monte Carlo scenarios. The control variables include the forecast real power generation of SPV and WT, real power of thermal generators except slack-bus, and voltages of all voltage generation buses. The total generation cost of the system is considered the main objective function to be optimized, including the penalty and reserve cost for underestimation and overestimation of SPV and WT, respectively. The proposed solution approach for OPF problems is verified on the modified IEEE 30 bus test system. The performance and robustness of the proposed hybrid PSO-GWO algorithm in solving the OPF problem is assessed by comparing the results with five other metaheuristic optimization algorithms for the same test system, under the same control variables and system constraints. Simulation results confirm that the hybrid PSO-GWO algorithm performs well compared to other algorithms and shows that it can be an efficient choice for the solution of OPF problems.

The determination of the transient stability of an electric power system is a crucial step in power system analysis. This paper investigates the transient stability of an IEEE-9 bus system consisting of three generators and nine buses. At first, a load flow analysis is conducted in order to determine the pre-fault conditions. Secondly, fault analysis is performed to analyze post fault conditions like the fast fault clearing time and load switching in order to determine the system stability. For transient stability analysis, Euler and Runga methods are compared and applied on the frequency and rotor angle of the system to analyze the system variations under different fault conditions. The simulations were done on the Power World Simulator (PWS) software. It is concluded that Critical Fault Clearing Time (CFCT) is a very important factor in keeping the power system within the stability bounds. A slight increase in Clearing Time (CT) from the critical value causes un-synchronism.

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.

In this research work, an automatic power factor correction (APFC) technique is presented to improve power factor of industrial loads to the desired value. Power factor is corrected to provide relief to utility by supplying required reactive power demand of load locally. The improvement of power factor of industrial customer will reduce customer monthly bill as well as will escape from financial penalty being imposed by utility owing to his low power factor. Different power factor correction (PFC) techniques exists, however we have proposed a new scheme of PFC using PIC microcontroller (18F452) chip. A microcontroller based algorithm is developed to measure power factor of load and examine the similarities or differences with the set referenced value of power factor to switch in required value of capacitors to correct the power factor of load to desired value. The developed scheme in perspective of controlling the power factor has a main advantage of choosing the direct value of capacitor that is needed to correct the necessitate amount of reactive power in order to cater the current utilization by the load. The prevailed results have affirmed that the developed scheme is able to yield a desirable power factor and can be furthermore pursued in practical applications.

Researches in personal identification show that classification using multiple mental thoughts increases complexity and system’s processing time. In this paper, an efficient classification algorithm is proposed to classify an individual using multiple mental thoughts. Features from Electroencephalography (EEG), used as biometric, are extracted using sixth order Autoregressive (AR) model, and Linear Discriminant Analysis (LDA) based classification is performed based on best mental thought combinations. Matlab® simulation results indicate that the proposed algorithm reduces the complexity as well as the processing time that confirms the use of EEG as a biometric for personal identification.